assessing the sustainability of projects: a fuzzy approach · 2014-05-12 · assessing the...

Assessing the Sustainability of Projects: AFuzzy ApproachSteven Carter1, Michael Negnevitsky1 and Alan Robertson2

1. University of Tasmania, Department of EngineeringP.O. Box 252C, Hobart, Tasmania 7001, Australia.

2. Pacrim Environmental Pty Ltd, 2/120 Derby Street,Newcastle NSW 2300. (Formerly EnvironmentalManager, Copper Mines of Tasmania Pty Ltd).

Emails: [email protected]@[email protected]

Abstract

This paper describes a method of assessing the sustainability impact of projects,based on using a fuzzy model to predict changes to an appropriate set ofsustainability indicators. It is argued that fuzzy techniques offer a natural way tohandle the “best-guess” or otherwise uncertain data which is typically availablefor model input. The method is applied to a major Australian mining operation,and predicted indicator changes are shown to agree well with estimated changes.

1 Introduction

Development applications are routinely supported by environmentalimpact assessments, which tend to focus on the immediate concernsassociated with the proposed project. A more strategic level of impactassessment is often lacking, which can lead to sustainability threats beingoverlooked, such as the cumulative impact of incremental development.

Transactions on Information and Communications Technologies vol 20, © 1998 WIT Press, www.witpress.com, ISSN 1743-3517

Sustainability is an accepted key goal of resource management andplanning, and there is thus a clear need for a practical method to assessthe sustainability aspects of proposed projects.

The ways in which a project might influence sustainability issues areoften complex, and characterised by feedbacks which make the problemhighly non-linear. However, it is important that an assessment methodbalances the need for credibility against the need for simplicity. Themethod must be sufficiently robust to be useful for applications based onless than complete information; and yet it must not demand a level ofeffort or an understanding of mathematics that puts it beyond the reach ofmost people for whom it is intended to be a tool. Fuzzy techniques offerways to overcome these problems. This paper describes a fuzzy-basedsustainability assessment method, and demonstrates its application to amajor mining operation, in Tasmania, Australia. It is shown how themethod can lead to improved planning decisions, and to better design ofbaseline surveys and monitoring programs.

2 The Sustainability Imperative

In Australia, as in many nations, the sustainability imperative is acceptedby all levels of government. Tasmania's Resource Management andPlanning System, for example, is an integrated legislative package, withsustainability as its principal objective (DELM1). The terms sustainabilityand (ecologically) sustainable development are used quiteinterchangeably in the literature, and many definitions of these termshave been offered [e.g. DELM1, WCED2, ESDSC3, IEAust4]. However, areview of the debate over the past ten years suggests that it is reasonableto assert that a sustainability assessment method should encourageplanning decisions which move us towards a vision of the sustainablefuture that we wish our children to inherit, a vision which is defined interms of three component themes:

1. Biodiversity: Biological diversity and ecological integrity.

2. Socio-economic: Wellbeing and equity within and betweengenerations.

3. Physical environmental: Regional and global environmental wellbeing.


Human quality of life depends on all three sustainability themes. It ismore than social well being, since our appreciation of the physicalenvironment and biodiversity extends beyond their uses as materialresources, and it is recognised that biodiversity has an intrinsic value,independent of humans.

Figure 1 shows a conceptual model of the main interactions betweenthese three themes, adapted from a model proposed by Hammond et al.5.It is important to note that purely financial, technical, military or politicalaspects of a project are not fundamental to the definition of sustainability,and only provide constraints on viable decisions. Also, the user-paysprinciple is considered to be a policy mechanism, not a sustainabilityprinciple.

Economy

Waste andpollution

People

SOCIO - ECONOMIC BIODIVERSITY

Ecosystems

PHYSICAL ENVIRONMENT

Pressure

Goods &services

Resources

Figure 1. Sustainability interaction model.

3 Sustainability Indicators and Indices

Sustainability can be quantified in terms of indicators, and a cohesive setof such indicators can provide input to a sustainability assessmentmethod. This approach is consistent with Agenda 21, which calls for thedevelopment of sustainability indicators (UNCED6). As shown in Figure2, the indicators should be grouped according to the three themes definedabove, selected to match the sustainability issues associated with theproject, and should respect an appropriate hierarchy of sustainabilityvisions.


IN T E R N AT IO N A L A N D N AT IO N A L

S TATE

L O C A L

V ISI O N IN D IC AT O R S

P R O JEC TSU ST A IN A B IL IT YIN D IC AT O R S

So cio - eco no m icP hysical env iro nm entalB iodiversity

e.g . G reenhousegas em issions

e.g . T h reatenedspecies

e .g . N o ise

Figure 2. Organisation of sustainability indicators.

Other approaches to selecting and organising sustainabilityindicators are possible, depending on how it is intended to use theindicators, and Hammond et al.5 review several suggestions in this regard.In general, biodiversity indicators are poorly established, but it isgenerally agreed that three levels of concern should be monitored, namelyecosystem diversity, species diversity, and genetic diversity (SEAC7).Indicators are conveniently grouped either taxonomically, or according tohabitat. Brown et al.8 recommend a list of 14 taxonomic groups, includingmammals, birds, reptiles, fish, angiosperms and gymnosperms. Reid etal.9 note that it may be convenient to differentiate between diversity ofwild species, and diversity of domesticated species. Socio-economic andphysical environmental indicators are both well established, these latterbeing conveniently grouped according to media (air, water and so forth).

Selecting appropriate indicators involves screening candidateindicators, and many sets of suitability criteria have been developed [e.g.DEST10]. However, while such criteria would be applied rigorously fornational reporting purposes, the sustainability issues associated with agiven proposed project may need to be described by a set of less thanideal indicators.


Aggregation is an important aspect of data processing. Figure 3shows how raw field data, indicators and indices together form the well-known information pyramid, which illustrates how aggregation simplifiesthe communication of information to decision makers and the media(Hammond et al.5, SEAC7). The degree of aggregation reflects the extentto which it is desired to integrate and simplify data, which has aptly beendescribed as a compromise between scientific accuracy and demand forinformation.

Indices

Indicators

Analyzed Data

Primary Data

IncreasingAggregation

Public

Policy Makers

Scientists

Figure 3. The information pyramid.

The dash line in Figure 3 shows that the difference between anindicator and an index is not distinct, and one encounters phrases in theliterature such as “using an index as an indicator of some condition”. Ingeneral, an indicator is a quantity which is closely associated with thevalue it represents (a sustainability value in this case), and a given valuecan be described either by many indicators, which provide a detailedpicture of the value, or by a smaller number of fingerprint indicators. Anindex is a somewhat more abstract quantity that results from aggregatingindicators, and it provides a bulk description of the value.

For example, consider an urban airshed. A detailed scientificdescription of its air quality would need information about the local air


pollution meteorology, and about the associated temporal and spatialdistribution of airborne contaminants. However, a regulatory authoritywould likely accept concentrations of six pollutants (particulates, CO,SOx, NOx, Pb and O3), as credible indicators of air quality. For thepurpose of providing air quality information to the public, it is commonfor two or more of these six indicators to be aggregated to form an airquality index. In the case of a Tasmanian population centre, woodsmokeemissions are the major concern, and particulate concentrations could beused as a fingerprint indicator, in lieu of calculating an air quality index.

The characteristics of a good index are the same as those of a goodindicator, with the caveat that the success of an index also depends on theselection of appropriate constituent indicators, and on the use of anappropriate aggregation method. In addition, it must be appreciated thatan aggregation process is not reversible: an average cannot bedisaggregated into its original set of numbers.

Harding and Eckstein11 review index methods suitable for State ofthe Environment reporting. All indicator aggregation methods todate havebeen classical, but expert system techniques such as fuzzy methods andartificial neural networks will probably offer ways to extend and improveon classical approaches. An aggregation function is usually selected byexamining the correlation of candidate indices to measurements of thevalue which the index is intended to reflect. Classical aggregationfunctions are usually based on the generalised mean, hα (Klir andYuan12):

αααα

α

−

+++= )...(1

),...,,( 2121 nn aaan

aaah

This produces a number of well known averaging functions, notablythe minimum and maximum operations (α → ±∞ respectively); thegeometric mean (α→ 0); and the harmonic and arithmetic means (α = ±1respectively). These functions can be extended by weighting, butweighting should only be used to reflect the importance of indicators incontributing to an index within a given sustainability theme, since thethree themes are of equal importance. Nijkamp et al.13 review methods ofdirectly estimating weights for a linear arithmetic weighting function.These include the trade-off method, the rating method, ranking method,scaling, and paired comparisons.

Hammond et al.5 review possible approaches to representingsustainability by highly aggregated indices, constructed within aframework that integrates the components of sustainability. Several


methods are based on extending the well known Pressure-State-Responseframework, often used for State of the Environment Reporting. Thisapproach is being taken by the U.N. Commission on SustainableDevelopment, and by the World Bank. Adriaanse14 suggests developingindices that reflect primary policies in the environmental, economic andsocial domains.

An obvious next step is to combine the group of indices into a singleindex of sustainability, and several such single-index methods have beenproposed. Moffatt et al.15 applied five proposed index methods toScotland with conflicting results: one index showed Scotland’s presentpath is sustainable, two indices showed it to be marginally sustainable,and two indices showed it to be unsustainable. The appeal of a singlesustainability index is obvious, but it is hard to believe that any singlenumber could ever provide an adequate basis for sustainable planningdecisions. Even within a sustainability theme, such extreme aggregationmay not be appropriate since, for example, no single index couldmeaningfully represent a situation in which soil is highly contaminated,but the air quality is pristine.

4 Assessment Method

The sustainability assessment method is built around a fuzzy modelwhich was developed using the MATLAB (Ver. 5) software package.Figure 4 shows the model flow chart. The model requires baselinesustainability indicator values, and project impact factors (PIFs) as input,as discussed below. It first computes the initial indicator changes inducedby the PIFs, and then iteratively computes subsequent indicator changesdue to the various feedback mechanisms which link the indicators.

In its simplest version, the model assumes that a single fingerprintindicator is adequate to describe each sustainability theme. Theseindicators are denoted in the model flow chart as Ind = {Bio, Soc, Env}.Sustainability indicators are measured on a dimensionless linear scale of0-10, normalised such that 2 and 8 correspond to the intervention valueand the sustainability threshold value respectively. This use of asustainability threshold value is similar to the distance-to-target approachdiscussed by Harding & Eckstein11.

Indicator values are mapped onto this linear domain from theirnatural units and scale either using a known function, or by interpretingthe linear scale in terms of the five crisp ranges {Bad (0-2), Poor (2-4),Okay (4-6), Good (6-8), Excellent (8-10)}. The Bad range denotes


criticality of the indicator value, and the Excellent range denotesachievement of the sustainability vision. The mapping is similar to theuse of sigma coordinates in an ocean model to produce a fine resolutionnear the sea surface and the sea bed.

INPUTS

1. Baseline indicator values. Ind ={Bio, Soc, Env}

2. Project impact factors which drive indicator changes:

FIRST STEP

Compute initial indicator changes,due to project impact factors.

Ind = Ind + (∆ Ind)initial

| ∆ Ind feedbacks | ≤ ε ?No

SECOND STEP

Compute additional indicatorchanges, due to feedbacks.

Ind = Ind + (∆ Ind) feedbacks

OUTPUTS

Final values of indicators andindicator changes.

Ind final

∆ Ind = Ind initial - Ind final

Yes

Figure 4. Fuzzy model flow chart.

Sustainability indicators, and hence the assessment method, aresensitive both to the geographic scale and to the time period beingconsidered. It is thus important that sustainability indicators be specifiedwith reference to their areas of influence, and time frames. Thisrequirement applies to the indicator baseline values, the interventionvalues and the sustainability threshold values.

Project impact factors (PIFs) drive changes to the sustainabilityindicators. A distinct PIF is specified for each indicator, with reference tothe same area of influence and time frame. In the simplest version of themodel, described above, there are thus three PIFs, one for each indicator.


PIFs are measured on a scale of -15 to +15, which is conveniently dividedinto seven crisp ranges:

Very Large Negative -15.0 to -12.5Large Negative -12.5 to -7.5Slight Negative -7.5 to -2.5None -2.5 to +2.5Slight Positive +2.5 to +7.5Large Positive +7.5 to +12.5Very Large Positive +12.5 to +15.0

A PIF can be made less negative by altering the project to includebetter environmental or biodiversity protection measures, or socio-economic provisions, which mitigate the adverse impacts.

A PIF only reflects the immediate impacts associated with theproject, and does not include feedback considerations. For example, anappropriate indicator set for a wastewater treatment plant might be waterquality (physical environment), aquatic ecosystem well being(biodiversity), and recreational usage potential (socio-economic). Thewater quality PIF is positive, since an improvement of water quality willbe a direct result of decreasing the contaminant load discharged to thereceiving water. However, the PIFs for the other two indicators are zero,since changes in these indicators result from the improvement in thewater quality indicator, and these feedbacks are computed separately bythe fuzzy model.

Figure 5 defines the membership functions used by the fuzzy model.For the project impact factor membership functions, Very Large Negativeis denoted by VLN, and so on. For the indicator change membershipfunctions, Very Large Decrease is denoted by VLD, and so on. Theindicator value membership functions correspond to the crisp rangesdiscussed above. For example, Excel reflects the extent to which theindicator value achieves the sustainability threshold.

5 Model Computational Steps

The model has two computational steps. The step one inputs are thebaseline indicator values and the project impact factors (PIFs). The stepone output is the set of initial indicator changes induced by the PIFs, andthe sizes of these initial changes must be consistent with the PIFs.


Indicator value membership functions. Bad and Excel are sigmoidal, while functionsPoor, Okay and Good are Gaussian.

Project impact factor membership functions. All are Gaussian.

Ind ic ato r C hang e (output fro m Step 1 , input to Ste p 2 )

Initial indicator change membership functions. All are trapezoidal. Feedback changemembership functions are similar, but defined over half the range (-3.5 to +3.5).

Figure 5. Fuzzy model membership functions.


The second step is an iteration which computes further indicatorchanges due to feedbacks. The absolute indicator values are adjusted bythe changes calculated by step one and, together with the set of theseinitial indicator changes, are input to the first iteration of the second step.The output from the first iteration is a new set of indicator changes, thistime due to the feedback fuzzy rules. The input and output indicatorchanges are denoted ∆Indold and ∆Indnew respectively.

The absolute indicator values are again adjusted, to take into accountthe new set of changes, ∆Indnew. Then ∆Indold > ∆Indnew, and becomesthe input to the next iteration. The sizes of the feedback changes fromeach iteration, Indnew, can be adjusted, but the overall changes calculatedby step two should be about the same magnitude, or less than, the initialchanges calculated by step one. This is achieved if the range of the outputindicator change functions is made smaller than the range of the inputindicator change functions by a factor of half (the model predictions arenot very sensitive to the exact choice of damping factor).

The step two indicator change magnitudes are tested against atolerance, ε , upon completion of each iteration. A choice of ε ≈ 0.1produces convergence within a few iterations, and the process isunconditionally stable. Larger tolerances produce premature terminationof the iteration, with crude results, while smaller tolerances lead tounrealistically high overall feedback changes.

Figure 6 shows the fuzzy inference rule matrix for the first step.Different fuzzy rules are specified for different absolute values of anindicator, to avoid increasing or decreasing indicator values beyond thelimit values of 0 and 10. For example, the first rule is: If (PIF is VLP) and(IndicatorValue is Bad) then (∆Indicator is VLI). A positive PIF is mosteffective when it acts on an indicator with a low value, and vice-versa fora negative PIF. The largest possible PIF (+15), and the lowest possibleindicator value (0), result in an indicator change of about 5½.

Input: Indicator ValueBad Poor Okay Good Excel

VLP VLI LI LI SI NCLP LI LI LI SI NCSP SI SI SI SI NCNone NC NC NC NC NCSN NC SD SD SD SDLN NC SD LD LD LD

InputProject Impact Factor

VLN NC SD LD LD VLDOutput: ∆Indicator

Figure 6. Fuzzy rule matrix for initial indicator changes.


Figure 7 shows the basic fuzzy inference rule matrix for the secondstep, giving examples of positive and negative feedback mechanisms.

Inputs: Sustainability indicator values.6444444444444474444444444448

Biodiversity Socio-economic Environmental

∆Bioold

Positive.Revegetated areawill expand oncestarted.

Positive. Improvedwildlife improvestourist industry.

Positive. Wetlandsremovecontaminants.

∆Socold

Negative. Urbanexpansion destroyshabitat.

Positive. Inner cityredevelopmentimproves businessand residentialpotential.

Negative.Expansion ofhuman activitiesadversely impactsthe environment.

Inputs:Indicatorchanges

∆Envold

Positive.Degradation ofwater quality willadversely impactaquatic ecosystems.

Positive. Betterwater qualityimprovesrecreational &resource potential.

Positive. Acid gasemissions result inacidification oflakes.

∆Bionew ∆Socnew ∆Envnew

1444444444444424444444444443

Outputs: Adjusted indicator changes, and hence new indicator values.

Figure 7. Fuzzy rule groups for typical indicator changes from feedbacks.

There is no unique specification for this matrix, and differentfeedback mechanisms may apply. For example, in the case studypresented below, the lunar landscape which characterises the Queenstownarea in Tasmania has resulted from adverse environmental impacts, butbenefits the community as a tourist attraction, so that it might be arguedthat the ∆Envold - ∆Socnew feedback is negative in this case.

Figure 8 shows part of the fuzzy feedback rule matrix in terms ofmembership functions and fuzzy rules, and Figure 9 shows the output of asingle positive rule group as a surface plot. As before, different rules areneeded for different absolute values of an indicator. The unshaded rulegroup in Figure 8 is for a negative feedback mechanism between socio-economic changes (∆Socold) and biodiversity changes (∆Bionew). Theshaded rule group is for a positive feedback mechanism between physicalenvironmental changes (∆Envold) and biodiversity changes (∆Bionew). Thefirst rules in these two groups are, respectively:

If (∆Socold is VLI) and (Bio is Poor) then (∆Bionew is NC)If (∆Envold is VLI) and (Bio is Poor) then (∆Bionew is VLI)


664444 Input indicator value 444488Biodiversity

Bad Poor Okay Good ExcelVLI NC SD LD LD VLDLI NC SD LD LD LDSI NC SD SD SD SDNC NC NC NC NC NCSD SI SI SI SI NCLD LI LI LI SI NC

∆Socold

VLD VLI LI LI SI NCVLI VLI LI LI SI NCLI LI LI LI SI NCSI SI SI SI SI NCNC NC NC NC NC NCSD NC SD SD SD SDLD NC SD LD LD LD

InputsOldindicatorchanges

∆Envold

VLD NC SD LD LD VLD∆Bionew

114444 Outputs: new indicator changes 4433

Figure 8. Fuzzy feedback rule matrix, showing positive (shaded)and negative (unshaded) feedback rule groups.

−6−4

−20

24

6

0

2

4

6

8

10

−2

−1

0

1

2

Indicator change (input)Indicator value

Indi

cato

r ch

ange

(ou

tput

)

Figure 9. Output surface generated by a single fuzzy rule group for(positive) indicator feedback changes.


The form of the feedback rule matrix must be checked beforerunning the fuzzy model for a given assessment. It must be agreed whichfeedback rule groups are positive, and which are negative, and it may beappropriate to assign different weights to the rule groups whichcontribute to a given output, the default assumption being that the rulegroups within a set all have the same weight. In the basic model, there arethree rule groups for each output and, considering for example the ∆Biooutput, the ∆Envold - ∆Bionew feedback mechanism might be twice asinfluential as either the ∆Socold - ∆Bionew feedback or the ∆Bioold -∆Bionew feedback, in which case these rule groups would be weighted inthe ratio ∆Bio:∆Soc:∆Env = 2:1:1.

In the present model, the minimum operator is used for inferences;the maximum operator is used for the AND operation; a summationmethod is used to aggregate the indicator change fuzzy sets produced bythe various rules; and the resulting single output membership function isdefuzzified by the centroid method.

6 Method Application

The principal strengths of the assessment method are its simplicity, andits use of indicators to link the model predictions to the more strategicassessment which sustainable planning demands. The method is a tool foruse by professional environmental practitioners. It facilitates explorationof the consequences of a proposed project, but it does not provide asimple answer regarding project go-ahead. Its limitations are defined bythe selection of sustainability indicators; the quality of data available tosupport these indicators; and by the understanding of the project’s initialimpact on these indicators, as defined by their associated project impactfactors.

A model extension is necessary if more than a single indicator (orindex) is needed to adequately describe a given sustainability theme. Forexample, physical environmental quality may need to be described bymedia-specific indicators, such as EnvAir, EnvSoil and EnvWater.However, the total number of indicators and indices should be kept to aminimum, to ensure that the model remains a practical tool, and to avoidindicator redundancy.

It is suggested that the following assessment exercises beconsidered:


• Examine both the short term and long term sustainability aspects of aproject; and ways in which the project will change the sustainabilityaspects of the local area compared to larger regions. For example, anew industrial facility may provide socio-economic benefits for alarge region, but have adverse local consequences.

• Examine the effect of changing the project impact factors. Forexample, the project might be scaled up or down, clean productiontechnology might be introduced, different pollution controlequipment might be assumed, or environmental & biodiversitymanagement measures might be changed.

• Run sensitivity tests to determine the extent to which the modelpredictions alter if the baseline indicator values are changed slightly.If the value of a particular indicator is of key importance to the modelpredictions, and thus to the subsequent planning decision, then thisknowledge can be used to design baseline studies to better understandthe indicator, to design appropriate monitoring programs, and also todetermine the need for use of precautionary principle.

A project might result in some indicators improving at the expenseof others, in spite of mitigation measures such as those suggested above.In these cases, it may be that a decrease in a given indicator can betolerated, or it may be possible to introduce measures to off-set the localconsequences, while preserving the project benefits.

7 Case Study: Mount Lyell MiningOperations

Figure 10 shows the West Coast of Tasmania. Queenstown, the regionalpopulation centre, is a mining town established to support the MountLyell mining operations, and the coastal community of Strahan lies 25km west of Queenstown. The region is quite isolated, and was linked tothe rest of Tasmania only by sea and rail transport before the LyellHighway was opened in 1932.

The history of Tasmania's West Coast is documented by Blainey16,and reviewed in an environmental impact context by SDAC17, McQuadeet al.18, and Thompson and Brett19. Mineral exploration in the region datesfrom the 1880s, with the discovery in 1883 of the "Iron Blow" copper


deposit, and prior to this discovery the area was essentially uninfluencedby human activities. Subsequent mining activities followed a frontiereconomics ethic, with little regard for environment protection, and by1900 Mount Lyell had become the largest copper mine in the BritishEmpire. The Tasmanian Environment Protection Act (1973) wasintroduced decades too late to mitigate the environmental damage whichis the legacy of the region's mining history.

Figure 10. Map of Tasmania's West Coast, showing thelocation of the Mount Lyell mine (courtesy Department of

Environment & Land Management).

Mining operations at Mount Lyell were scaled down after about1970, and the mine closed in late 1994, having produced over 1.3 milliontonnes of copper, 750 tonnes of silver, and 45 tonnes of gold, equivalent


to over A$4 billion in 1995. The Mount Lyell Remediation Research andDemonstration Program was established in 1995 to study environmentaldamage in the Queenstown region, and to design a remediation strategy.The program was managed by the Federal and Tasmanian Governments,and comprised 14 investigative projects. Koehnken20 summarises theresearch findings, and outlines a remediation plan targeting primarypollution sources, notably the mine lease site.

Several companies expressed interest in continuing the mining lease,and Copper Mines of Tasmania Pty Ltd (CMT) became the new mineoperator, reopening the mine in late 1995. The Copper Mines ofTasmania Agreement Act (1994) indemnifies CMT for both existing andongoing environmental harm caused by previous mining activities, butthe company’s operations are guided by an Environmental ManagementPlan within the context of this agreement (Thompson and Brett19).

1880s - 1920s AssessmentTable 1 sets out sustainability indicators, change mechanisms and projectimpact factors (PIFs) for the 1880s to 1920s period, based on a review ofSDAC17, McQuade et al.18, and Thompson and Brett19. Table 2 gives themain indicator feedback mechanisms for this period, identifies thefeedback rule groups as positive or negative, and assigns weights to therule groups. The assessment focuses on Queenstown, for modelvalidation purposes. However, the mining project also influenced thesustainability of the west coast region as a whole, of Tasmania, and of theBritish Empire beyond Tasmania. This extended influence was mainlysocio-economic in nature, but support transport infrastructure such as theLyell Highway impacted the environment to a degree.

Each sustainability theme in Table 1 has more than one indicatorbut, to a good approximation, each theme can be characterised by a singlepattern of behaviour as it relates to the start-up of Mount Lyell miningactivities. For example, the biodiversity theme can be divided intofingerprint indicators relating to aquatic and terrestrial ecosystems, butboth have responded to the mining operations in similar ways. Thephysical environmental and biodiversity PIFs of the Mount Lyell miningoperations during the initial decades derive from the footprint of thesurface mining operations, infrastructure and timber harvesting, acid gassmelting emissions, waste water discharges, and tailings disposal. ThesePIFs were highly negative, since they were not mitigated byenvironmental or biodiversity management measures. The socio-economic PIF, however, was high and positive. By the 1920s,


Queenstown had become a prosperous and stable community of about6,000 people, albeit remote from other population centres.

Table 1 also shows the estimated sustainability indicator values inthe 1880s, and in the 1920s. Prior to the start of mining activities, in theearly 1880s, there was little socio-economic activity in the region, and thephysical environmental and biodiversity features of the region wouldhave been similar to the temperate rainforest conditions observed today inthe nearby World Heritage wilderness areas. By the 1920s, the combinedeffects of smelting, timber harvesting, and soil erosion had produced a"lunar" landscape, which characterises the area to the present. Similarly,wastewater and tailings discharges had severely impacted the quality andbiodiversity of the receiving waters, notably the Queen River and thelower King River.

1920s - 1995 Indicator ChangesFrom the 1920s to 1995, the impact potential of the mine reduced asoperations moved underground, smelting ceased, and environmentalmanagement improved. During this period, however, the Lyell Highwaywas built, and the transport infrastructure serving Queenstown wasupgraded. Overall, it is judged that the physical environmental indicatordid not change significantly, while the biodiversity indicator merelyincreased from 1 to 2, as some limited revegetation occurred.

In 1970, Queenstown’s population was still over 5,000 people, withsome 1,200 people directly employed by the Mount Lyell mine operator.However, mining operations were subsequently scaled down, and thetown's population declined to about 3,000 people in 1994, with only 330direct mine employees. Closure of the mine in late 1994 resulted in afurther population decline to about 2,500 people in 1995, and SDAC17

identified other impacts of mine closure as increased unemployment,reduced school enrolments, a reduction in business activity, an increase invandalism, a transfer of dependence from industry to government, aperiod of grieving, and a reduction in community self-esteem. Theseimpacts were ameliorated by expectation of the mine being reopened byCMT, and because improved transport reduced the degree to which thecommunity was isolated. It is estimated that the socio-economic indicatorhad decreased from 7 to 5 by 1995.


Table 1. 1880s–1920s: Sustainability indicators and values, change mechanisms, and project impact factors.

Sustainability indicators Values in 1880sProject impact factorsand indicator change mechanisms Values in 1920s

BiodiversityAquatic ecosystems. Queen River, andof the lower King River andMacquarie Harbour.

Habitat extent & quality. Generally the15 km2 area centred on Queenstown,and along the banks of the Queen andlower King Rivers.

9 (Excellent)Near wildernessvalues.

PIF = - 15Vegetation on the hills surroundingQueenstown was destroyed by treeharvesting, and by mining and urbandevelopment.

1 (Bad)All life in the Queen River and the lowerKing River had been killed. Biologicalcommunities in Macquarie Harbour wereimpoverished compared to coastal bayselsewhere in south-east Australia.Vegetation in the Queenstown area hadbeen destroyed, together with most bankside vegetation along the rivers

Socio-economicPopulation, average income andemployment rate, and access to otherpopulation centres. Indicators relate toQueenstown only.

3 (Poor)Small pioneercommunity.

PIF = + 15Mining activities resulted in rapidgrowth of Queenstown andsupporting rail infrastructure.

7 (Good)Queenstown was a prosperous and stablecommunity of about 6,000 people. Workershad high average incomes, unemploymentwas low., but the town was remote fromother population centres.

Physical environmentalWater quality. Same domain as foraquatic ecosystems.

Soil quality. Same domain as forhabitat.

9 (Excellent)Near wildernessvalues.

PIF = - 15Tailings, slag and acid mine drainagewere discharged into the rivers andthus into Macquarie Harbour. Acidgas emissions were produced bysmelting. Mining and urbandevelopment degraded soil quality.

1 (Bad)Waters are acidic, and contaminated withtoxic metals, particularly copper. The riverbanks were smothered with tailings, and adelta of tailings had been created in theMacquarie Harbour. All soil had erodedfrom the Queenstown area.


Table 2. 1880s–1920s: Principal feedback mechanisms for the fuzzy rule matrix for indicator feedbackchanges. Positive rule groups are shaded, negative rule groups are unshaded.

664444444444444444 Inputs: Sustainability indicator values 444444444444444444444488

Biodiversity Socio-economic Environmental

∆BiooldPositive: Weight = 1Revegetated areas expand oncestarted.

Positive: Weight = 1Degraded ecosystems adverselyimpact community’s naturalresource base.

Positive: Weight = 2Destruction of vegetationresults in soil erosion.

∆SocoldNegative: Weight = 1Urban development destroys &fragments habitat.

Positive: Weight = 4Mining activities supportimprovement in businesses,services and amenities.

Negative: Weight = 1Urban developmentadversely impacts theenvironment.

InputsIndicatorChanges

∆EnvoldPositive: Weight = 3• Degradation of water

quality impacts aquaticecosystems.

• Vegetation destroyed byacid fumes, revegetation isprevented by erosion.

• Tailings destroy bank sidevegetation.

Negative: Weight = 1Degraded environmentadversely impacts community’snatural resource base (e.g.drinking water).

Positive: Weight = 2Smelter emissions acidifysoil & water.

∆Bionew ∆Socnew ∆Envnew

114444444444444444444444 Outputs: Adjusted indicator changes. 44444444444444444433


Table 3 summarises the estimated changes in sustainability indicatorvalues, from the 1920s to the reopening of the mine by CMT in late 1995.

Table 3. Sustainability indicator changes 1920s to 1995.Indicator 1920s value 1995 valueBiodiversity 1 (Bad) 2 (Bad/Poor)Socio-economic 7 (Good) 5 (Okay)Physical environmental 1 (Bad) 1 (Bad)

1995 - 2000 AssessmentsTwo sustainability modelling exercises are considered for the period1995-2000. The first is an assessment of continued mining operations byCMT; and the second is an assessment of the scenario in which MountLyell mining operations had ceased permanently in late 1994. Althoughmining remains the predominant regional economic activity, the Strahanmariculture industry has expanded since the 1950s, and eco-tourismbusinesses based in Strahan have also become an established. Pollutionfrom mining has the potential to adversely impact on the success of thesebusinesses, and a detailed assessment of the mine's sustainability impactwould need to include the community of Strahan. However, for thepurpose of model validation, it is sufficient for the socio-economicindicator to still be defined as only relating to Queenstown and, with thisfocus, it is apparent that the continuation of mining operations ispreferable to mine closure.

Considering the physical environmental and biodiversity indicators,CMT has worked with the State Government on a range of initiativessuch as acid drainage prevention, improved tailings management, andrevegetation projects. Had the mine closed permanently, however, theState Government would likely have undertaken some of these initiativesanyway, since the ongoing pollution potential of an abandoned mine issignificant (Waldichuk21).

Table 4 sets out the indicator change mechanisms, project impactfactors, and estimated indicator values for these two assessments, basedon a review of SDAC17, and on discussions with CMT. The feedbackmechanisms are assumed to remain the same. Figure 11 shows the modelpredictions for the three sustainability assessment exercises describedabove, and Table 5 summarises the estimated and predicted indicatorvalues. The clear success of the fuzzy model offers hope that thesustainability assessment method presented herein can be used as thebasis for the more integrated and broad-based planning decisions whichthe sustainability imperative requires.


Table 4. 1995–2000: Sustainability indicators, change mechanisms, project impact factors and estimated indicator values.

1995 Indicator values 2000, Mine Open 2000, Mine Closed

Biodiversity2 (Bad/Poor)

PIF = +6. Indicator = 4 (Poor/Okay)

Some revegetation continues, assisted bylandcare programs, particularly along the riverbanks.


As for the mine continuation scenario.

Socio-economic5 (Okay)

PIF = +4. Indicator = 7 (Good)

The mine continues to benefit Queenstown,but with a reduced number of direct mineemployees.

PIF = -3. Indicator = 4 (Poor/Okay)

Queenstown community suffers from lack ofmining activity, but impacts are cushioned bythe tourist industry.

Physical environment1 (Bad)


The proposed mine wastewater treatment plantwill reduce contaminant loads discharged tothe rivers. The new tailings dam also resultsin environmental improvements.

PIF = +1. Indicator = 2 (Bad/Poor)

As for the mine continuation scenario, butimprovements are limited since improvementsto the mining lease are not driven by an activeenvironmental management plan.


1880s to 1920s

1995 to 2000, Mine Open

1995 to 2000, Mine Closed

1 2 3 4 5 6 7 8 9 10 11 120

1

2

3

4

5

6

7

8

9

10

Step Number

Indica

tor Va

lue

Physical Environmental

Socio−Economic

Biodiversity

1 2 3 4 5 6 7 8 9 100

1

2

3

4

5

6

7

8

9

10

Step Number

Indica

tor Va

lue


Socio−Economic

Biodiversity

1 2 3 4 5 6 7 8 9 10 110

1

2

3

4

5

6

7

8

9

10

Step Number

Indica

tor Va

lue


Socio−Economic

Biodiversity

Figure 11. Model predictions of sustainability indicator changes.


Table 5. Estimated vs predicted sustainability indicator values.

1920s 2000, Mine Open 2000, Mine Closed

Indicator 1880s Estimated Model 1995 Estimated Model Estimated Model

Biodiversity 9Excellent

1Bad

1.5Bad

2Bad/Poor

4Poor/OK

4.6Okay

4Poor/OK

4.1Okay

Socio-economic 3Poor

7Good

7.5Good

5Okay

7Good

7.6Good

4Poor/Oka

y

3.4Poor

Physicalenvironmental

9Excellent

1Bad

1.5Bad

1Bad

4Poor/Oka

y

4.1Okay

2Bad/Poor

2.2Poor


A c k n o w l e d g e m e n t s

The authors are grateful to the Tasmanian Department of Transportfor funding this research, and would also like to acknowledge themany helpful comments provided by DOT staff, John Hunter(CSIRO Division of Marine Research), Peter Doe (University ofTasmania) and Barry Tapp (UNITEC Institute of Technology,Auckland).

R e f e r e n c e s

[1] DELM. Information Guide to the Resource Management andPlanning System, 2nd Edition. Environment & Planning Division,Department of Environment and Land Management, Hobart, 1996.

[2] WCED. Our Common Future. World Commission on Environmentand Development. Oxford University Press, 1987.

[3] ESDSC. National Strategy for Ecologically SustainableDevelopment. Ecological Sustainable Development SteeringCommittee, Australian Government Publication Service, Canberra,1992.

[4] IEAust. Institution of Engineers, Australia, Policy onSustainability. Institution of Engineers, Australia, NationalCommittee on Environmental Engineering, Canberra, November1994.

[5] Hammond, A., A. Adriaanse, E. Rodenburg, D. Bryant and R.Woodward. Environmental Indicators: A Systematic Approach toReporting on Environmental Policy Performance in the Context ofSustainable Development. World Resources Institute, May 1995.

[6] UNCED. Agenda 21. An action plan for the next century. Endorsedby the United Nations Conference on Environment andDevelopment, Rio de Janeiro. United Nations Association, 1992.


[7] SEAC. Australia: State of the Environment 1996. An independentreport to the Commonwealth Minister for the Environment by theState of the Environment Advisory Council. CSIRO Publishing,Collingwood, Victoria, 1996.

[8] Brown, A., A. Young, J. Burdon, L. Christidis, G. Clarke, D.Coates and W. Sherwin. Genetic Indicators for State of theEnvironment Reporting. State of the Environment Technical PaperSeries (Environmental Indicators), Department of the Environment,Sport and Territories, Canberra, 1997.

[9] Reid, W.V., J.A. McNeely, D.B. Tunstall, D.A. Bryant and M.Winograd. Biodiversity Indicators for Policy Makers. WorldResources Institute, October 1993.

[10] DEST. State of the Environment Reporting: Framework forAustralia. Department of the Environment, Sport and Territories,Canberra, 1994.

[11] Harding, R. and D. Eckstein. A Preliminary Discusssion Paper onDevelopment of Composite Indices for State of the EnvironmentReporting in NSW. Institute of Environmental Studies, Universityof New South Wales, April 1996.

[12] Klir, G. J. and B. Yuan. Fuzzy Sets and Fuzzy Logic: Theory andApplications. Prentice Hall, New Jersey, 1995.

[13] Nijkamp, P., P. Rietveld and H. Wood. Multicriteria Evaluation inPhysical Planning. North-Holland Publishing Company,Amsterdam, 1991.

[14] Adriaanse, A. Policy Performance Indicators. Ministry ofHousing, Physical Planning and Environment, the Hague, 1993.

[15] Moffat, I., N. Hanley and J.P. Gill. Measuring and assessingindicators of sustainable development for Scotland: a pilot survey.International Journal of Sustainable Development and WorldEcology, 1, pp170-177, 1994.

[16] Blainey, G.N. The Peaks of Lyell. 5th Ed (1993), St Davids ParkPublishing, Tasmania, 1954.


[17] SDAC. Copper Mines of Tasmania Project Final AssessmentReport, Sustainable Development Advisory Council, Hobart,Tasmania, August 1995.

[18] McQuade, C.V, J.J. Johnston and S.M. Innes. Mount LyellRemediation: Review of historical literature and data on thesources and quantity of effluent from the Mount Lyell Lease Site.Supervising Scientist Report No. 106, Commonwealth of Australia,1995.

[19] Thompson & Brett. Copper Mines of Tasmania Mt Lyell MineRedevelopment Environmental Management Plan, Thompson &Brett Pty Ltd in association with Environmental Scientific ServicesPty Ltd, Hobart, Tasmania, August 1995.

[20] Koehnken, L. Mount Lyell Remediation: Final Report. SupervisingScientist Report No. 126, Commonwealth of Australia, 1997.

[21] Waldichuk, M. Pollution by Abandoned Mines. Marine PollutionBulletin, Vol. 18, pp. 422-423, 1987.


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